Q: Hi, Ashish, welcome to the Big Unlock podcast. I’m Rohit Mahajan, CEO and Managing Partner at Damo Consulting. It’s fantastic to have you back on the podcast after a long while. I’m sure things have come a long way. Would you like to start with your introduction?

Ashish: Happy to, Rohit. It’s always a pleasure to talk to you and be on the podcast. I’m Ashish Atreja here at UC Davis Health, and I’m also a venture partner at Global Ventures. I have a mission to create clinicians as co-creators and co-founders for mission-driven initiatives in that regard. I’ve been in the role of Chief Innovation Officer in Medicine at Mount Sinai. Before that, I was an informaticist at Cleveland Clinic. I still practice gastroenterology one day a week at UC Davis Health. I worked as a CIO and Chief Digital Health Officer at UC Davis Health, supporting transformations at scale.

Q: That’s awesome, Ashish, that you’re able to pack so much into one day. Could you please share with us what motivated you on this journey? How did it all start, and how have you been navigating so far? Where are you headed? I think you have a very interesting group which is Valid AI, so perhaps talk about that as well.

Ashish: Absolutely, Rohit. I consider this my fifth career life, and I feel very lucky. I started off as a physician and a public health scholar. My first day of residency at Cleveland Clinic, I saw a lung transplant patient. It was the time when lung transplant was just getting into practice, and the books I used to read didn’t mention how to take care of a lung transplant patient.

There was no knowledge in the books because it took about five years for new knowledge to be printed in books. This was around 1999-2000. Someone told me to look at UpToDate, which was the first good online textbook. I managed my first patient learning from that online textbook. After successfully managing the patient, a spark idea came to me: Wow, it was just a database, but technology made it so easy for knowledge to flow. This was something that was never taught to us in medical school.

I could see, even in 1999-2000 convergence, how much technology could enable us. That led me to pursue informatics and IT. I did an informatics fellowship at Cleveland Clinic, supported inpatient EPIC implementation, and then began my journey in innovation. That was my second life, moving beyond informatics to innovation at Mount Sinai. Then, it was transformation at scale at UC Davis Health. Over the last 20-25 years, I’ve been initially 50 percent and now close to 100 percent in IT, digital health, and AI, with the same mission: How can we leverage technology to make us better physicians, better clinicians, and better scientists?

Q: That’s awesome. You did mention AI, Ashish, and you have a very big initiative around AI with Valid AI. Could you explain what motivated you, what it’s all about, and where it is heading?

Ashish: Absolutely, I’d love to. Earlier, when I was working in digital health, we helped establish one of the first digital health labs in the country at Mount Sinai. We felt the need to have an evidence-based way of looking at digital health applications. At that time, I started Node Health, a network of digital evidence in health—a nonprofit that brought health systems together to independently evaluate digital health applications. I felt the same need in the age of AI, especially generative AI. There are so many solutions coming down the pike—so many algorithms, so many products. How do we even evaluate them? How do we integrate them into our systems? How do we create value from them? Within our health system, we can still work on these solutions, but we’re basically repeating each other’s efforts. If I’m evaluating a solution, another UC is likely doing the same thing somewhere else. We simply don’t have the bandwidth in our health system to duplicate all this work.

Valid AI was created with the goal of bringing people together—to learn together, work together, and create efficiency. This program was endorsed by the UC Office of the President, and we officially launched it at UC Health about a year and two months ago.

We initially had most states in the U.S. represented, with around 38 to 40 health systems and health plans joining the network. We meet biweekly to discuss a range of topics—from organizational maturity and governance to identifying high-impact use cases and necessary monitoring. The goal is to help systems learn from each other and create an efficient approach to transformation.

As we enter year two, VALID stands for Vision, Alignment, and Learning—those were our focus areas in the first year. This year, we’re adding Implementation and Dissemination of Science as our next set of goals. As we receive feedback and grow, the network has expanded to 54 organizations. We’re now thinking seriously about how to implement AI at scale—not just for VALID AI founding partners, but beyond, to extend its benefits to a broader audience. We also want to make AI more affordable and accessible to everyone. To support that, we’re building shared teams designed to empower the entire healthcare ecosystem. It’s a very exciting and important next step for us.

Q: That’s awesome to know. I never knew the full form of VALID like you just described—thanks for explaining it so clearly, Ashish. It’s truly an ocean out there, as you mentioned. Tens, if not hundreds, of health systems are on this AI journey. With such a large landscape, how do you drive meaningful transformation? And what are some of the capabilities you’re working to build around that?

Ashish: Great question. I look at the external landscape and then the internal landscape within organization. I believe there’s enormous potential to create efficiency across the healthcare stream. To achieve that, we need more than isolated efforts—we need a network of organizations working together. So, I look very closely, I’m a part of the working group for Gen AI for CHAI, Coalition of Healthcare AI. So, we need to look at FDA and the governance from the federal government, but then we need something like CHAI to kind of create frameworks. And, and, you know, guidance back in that regard, but then we also need something like train, which I’m part of as well from UC Davis Health, which looks at the train network, looks at how do we kind of really distill things to more infrastructure level and kind of monitor that in a safe manner where VALID AI positions is more on impact.

How do we follow the guidelines from CHAI and the federal government, but how do we actually bring it to reduce to practice is what we call rubber meets the road and really create an impact. So, it really has a return on investment for us in that regard. So, that’s the learning part. But when we look at how do we really create an impact within our organization, moving from, and I had a great discussion with some of the folks and I was saying, all these external network organizations actually are interdependent and support each other.

For example, if we implement something and give feedback to CHAI to, Hey, your open model card really is working and this is how we’re using it. That supports CHAI vision as well.  we came with the concept of this Olympic rings, you know, seven Olympic rings or five Olympic rings, which are interconnected.

Yeah. So, we need interconnected different organizations outside to support us from that. But when we look at the landscape for internal to an organization, let’s say UC Davis Health as example, where I’ve been leading the charge on transformation, digital health and AI for the last four years, we need to look first, look at our AI strategy or our overall health system strategy.

Is our focus being in a quaternary hospital? Do we want to expand geographically through care at home? What are the major things we as an organization want to achieve? Because that major strategy then governs how we put AI into it, right? And so, it starts with a strategy-first approach, and then we distill from strategy to governance, which means.

How do we bring AI, which all approvals we have to need, we need cybersecurity team to look at it. We need approvals from ethics and equity compliance kind of committees, whatever structures are there. We need to look at someone from procurement, how they procure in that regard, whether it’s in UCS. We need to have some governance structure, which not only helps us move this along, but also the governance structure also helps us prioritize. Because there are so many possibilities right now, we can and we should not in any way bring all the AI technologies. We cannot and should not do that. We need to look at, out of all those technologies, which is completely aligned with our strategy, that meets our gap. So strategic lens, and then using the strategy lens, prioritize the top five or ten. Then those top five or ten then go through the governance and the implementation and the procurement process.

Once the procurement process happens, then we call it AI adoption roadmap needs to come in, where we have project manager, project coordinators, integration experts, after the procurement, actually putting it together. Then the go-live happens. And then we also need to do evaluation at scale and monitoring at scale.

We need to measure that the algorithm doesn’t degrade. We also need to know that we are creating an outcome which we want. Many times we are guilty of taking a new technology, just implementing it and not measuring its impact and not monitoring it. So I think there’s a new—we call it implementation science—layer that has to be built in.

We published a paper in New England Journal of Medicine AI about the need for creating AI implementation science centers in health systems. Very interesting and right, and I’m a gastroenterologist, so I often crack the joke: you know, the food is there, but our stomach has to be empty enough and there has to be hunger so we can, ingest the food.  Once we ingest the food, then we can digest the food. Right. So we need to increase our capacity as health systems to be able to ingest the right kind of AI solutions, and then to be able to digest and create the maximum value from the solutions.

Q: That’s awesome. So, Ashish, since you have been involved with AI and now GenAI for some time, could you narrate any use cases that you might have—being involved with yourselves or people that you know have done successfully? What are some of the things on the landscape that are making it into the health system? And what are some of the future directions that you see over there as well?

Ashish: Yeah, happy to. So, when we started doing research work with Valid AI, we are looking at every single publication, preprint or major journal publication that’s coming for used case. So, we’re setting up a use case library of 165 used cases. And the domains we are finding are either on the employee and physician productivity. So, we are seeing like AI scribe is perfect in that space on physician productivity, but we also seeing emergence of tools which will cater to all employees of the healthcare worker, not just the clinical workforce. And we are kind of looking at those possibilities.

Then we’re also looking at things at the patient automation side. Can we bring generative AI solutions which can guide the patients wherever they are? Because we have an access problem. And it’s not easy just to pick up a phone and talk to anyone in the health system. But can some of the routine work be done by generative AI solutions and guide the patients automated or semi-automated with humans in the loop sometime. Then we also seeing operational efficiency solutions like RCM and those which can allow better enhanced capture. So, we’re seeing kind of a three-legged kind of approach to that.

And we are using a PRISM kind of a method from Gartner, which looks at feasibility as well as time. And we are putting a next layer—is impact. So, you look at the lens from whatever is feasible today and the time to onboard that is not much, and it creates a maximum impact that can help you prioritize the most relevant used cases. In fact, because it’s not about bringing AI, it’s about solving what problem. Yes. And then you find the relevant things, AI or non-AI, to be able to do that. So, part of us is also educating our executives and leaders to start with a design thinking approach or problem-first approach, a strategy-first approach.

And that’s something our phase two of Valid AI looks like, where we create kind of a way to align everyone around strategy and problem-first approach and the most meaningful, impactful use cases that come. And then we are able to measure the impact. So, few cases that we already have implemented. One is AI scribe, and I was talking two years ago, it’s going to be like water. Everyone is going to be using AI scribe. Yes. Because—and this is the first time, Rohit, I have seen the chairman kind of arguing with each other who should go first. Well, technology typically has a productivity paradox when it comes to healthcare. Every industry has benefited with productivity except us in healthcare. But I do feel finally the time is here when we unlock the productivity paradox with technology for healthcare. We generate AI, and AI scribe is a perfect example of that.

So, we implemented in pilot phase and we just got ravishing reviews, including physicians just writing that this is the best technology they have seen till date. And this is a physician who’s about to retire.  We didn’t have to train them.

We’ve also brought technologies in vision AI. Something like Aidoc, where we were able to find that more PEs can be detected at an earlier time. So, we fast-tracked that implementation as a platform. So, we have seven sectors in AI from a strategic lens.

So we’ve been able to bring applications from risk prediction, like sepsis algorithms built in LA, then we fine-tuned that to things which can predict how the person is going to go worse based on alerts coming from wearable sensors, to vision AI—computer vision technologies, to technologies like generative AI—from AI scribe, but also then looking at and understanding generative AI. Can we unleash it to all the employees, bring the policies and other together to make every employee more powerful and Impactful.

Q: That’s awesome. So, Ashish, it was really nice meeting you at JPM, a few days ago, actually in sunny San Francisco this time. And, I was at your launch, you know, would love to learn more about what you talked about at the launch on the venture partnership and the kind of startups that are coming through the Venture Studio. Could you share some more information about that?

Ashish: Yes, Rohit. Happy to. I think throughout this informatics and innovation journey, I was lucky to be part of a spin-out from Mount Sinai, which took our app prescribing platform. We were the first ones where doctors could finally prescribe apps, in addition to medicine and surgery, and that got patented and led to a new company launching a startup. I was very grateful to be on the board and see the VC-backed company finally get acquired last year after touching 30 million lives directly and 60 million through UnitedHealthcare. So, in total, 90 million lives touched.

As I looked back on the journey, especially in the age of AI, I feel we can have a better way of partnering or creating startups in partnership with health systems and health plans, maybe embedding our soul. I look at startups as raising a kid. You have to put your heart and soul into it. Initially, their capacity is they need to learn how to poop, they need to wear diapers. But then, they build their capabilities, and that takes time. You need to be patient during that process, nurture them.

So part of it was, especially with generative AI, how can we create an ecosystem where we learn as healthcare organizations how to better partner with startups? Maybe those startups are co-created by different health systems and health plans, that led to the concept of GVX Global Venture Accelerator, where we are creating soulful startups, but in partnership with healthcare organizations. So they are not created outside by someone and then trying to disrupt you inside. They are actually from the very beginning supporting inside-out transformation, right? Because their soul is actually from clinicians as co-founders—a health system-led initiative.

I was very happy to see that launched at JPM. We also launched, along with that, our first startup, called Fusion Care, which is planning to support health systems, health plans, and individual practices in obesity centers for excellence. With the GLP-1, I do feel the biggest trends we have seen in the last few years for us have been, one is obesity and metabolic syndrome, which I, as a gastroenterologist, never felt could be treated by a physician, except for pediatric surgery. Now, this entire, the biggest disease we have of modern civilization, can now be treated with medicine.

But there are many more things that have to come together to treat it, including a whole care team of nutritionists, psychologists, a whole digital plan, which automates the patient follow-up, and putting them into the right reimbursement model. So, Fusion Care is launching and enabling a kind of practice enablement for obesity centers of excellence and metabolic centers of excellence.

And then we are working from a valid AI perspective, how do we bring other things together to fruition of shared services in general in that regard.

Q: That’s awesome. I was there at the panel discussions, and listening to the FusionCare CEO was fantastic. How they’re intending to make this change from inside out, as you’re describing, is the right way to do it, Ashish.

So, Ashish, as we come up on the end of the podcast, are there any other plans for the future or any new things that you are working on that you would like to share with the audience?

Ashish: This is good. Thank you. I have devoted, I think, for the next phase to, as I was supporting transformation at scale at UC Davis, really taking some time off to support the same journey for multiple health systems, health plans, and pharma together. Bringing things together, kind of expanding from the Valid AI experience into that.

I feel this will be fascinating because, can we, through a Venture Studio or Venture Accelerator, over the next 10 years, touch 1 billion lives? That’s the goal which I have put. We touched 30 million, 60 million lives in the past one. But how great would that be? As a physician, I would not be able to meaningfully touch more than 10,000 patients in the traditional care I can deliver. But through technology as an enabler, we have the potential to touch masses if we do the things right. Very  large footprint.

So, I’m looking for very meaningful partnerships with health plans, health systems, and life sciences to co-create entities together. So we don’t have to follow someone else leading and disrupting us and creating all those barriers. We can evolve ourselves, kind of a true metamorphosis that happens from inside out. So that’s what we are on the journey for, and I’d love to hear from others, get them as partners, and together we can shape this next phase of our exciting journey.

We hope you enjoyed this podcast. Subscribe to our podcast series at www.thebigunlock.com and write to us at info@thebigunlock.com   

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

Q: David, welcome to the 150th episode of The Big Unlock Podcast. Padmanabhan had done a lot of the previous episodes of this podcast and I’m carrying on his legacy.

So, David, would you take a few minutes and like to introduce yourself to the audience and talk about yourself and the health system as well where you have been working? 

David: Yeah, sure. Thanks a lot for hosting me. It’s an honor to be here with you this afternoon.

I’m David Berger. I started out as an academic surgeon and surgical oncologist. I did my general surgery training at SUNY Downstate and a fellowship in surgical oncology at MD Anderson Cancer Center in Houston, Texas. I initially thought I would become an academic surgeon—maybe a surgical oncology division chief, chair of surgery, or even a dean someday—but my career trajectory shifted due to unique opportunities.

Early in my career, I undertook a management role as the Chief of the Operative Care Line at the Houston VA, the Michael E. DeBakey Medical Center. It’s the largest, most complex, and busiest VA medical system in the country. Around that time, as part of the sandwich generation, I was also taking care of both my children and my parents. I realized how difficult it was to navigate the healthcare system. If it was challenging for me—someone who knows the system and has contacts—I couldn’t imagine how hard it must be for those without the same resources.

This experience shaped my career goal: to make healthcare simpler. The healthcare system is extremely complex, and I saw the potential of digital tools to transform care delivery, making it more accessible and effective. While at the VA, I implemented sophisticated digital health tools. One notable project involved real-time location systems (RTLS) for managing our operating rooms. We reduced turnover time between cases by nearly 50% and achieved a 95% on-time first case start rate—remarkable for any healthcare system, not just the VA.

Building on this success, I moved to the private sector as Chief Operating Officer at Baylor St. Luke’s Medical Center. There, we implemented machine learning tools to predict which patients were at risk of sepsis-related decompensation. By intervening early, we reduced sepsis mortality by almost 50%. This was back in 2014, well before the recent wave of AI publicity, but it reinforced my belief that digital health, AI, and machine learning are poised to revolutionize healthcare.

After Baylor St. Luke’s, I returned to SUNY Downstate, where I had completed my training, to serve as the Chief Executive Officer of University Hospital at Downstate. I started in September 2020 and recently left to focus on my passion: digital health and artificial intelligence in healthcare.

Now, I advise early-stage startups, participate in advisory boards and venture studios, and am about to become an LP in early-stage venture funds supporting digital health companies. That’s a little about me and my journey.

Q: That’s really great to know, David. It’s fantastic that physicians like yourself are taking on this journey because your deep domain knowledge can drive meaningful change in digital health.

I also learned that you’ve been an entrepreneur. Could you share a bit about your entrepreneurial journey and the kinds of software or digital solutions you worked with?

David:  So, about 10–12 years ago, I was approached by an early-stage startup company based in Canada. They had come across an article we published on early warning signs for readmission after colon cancer surgery.

We had identified at least 10 early warning signs and developed a machine learning algorithm to predict who was at risk of being readmitted. This software company wanted to integrate our findings into their tool. We conducted a couple of successful pilots and started discussing a long-term relationship. The idea was that we, as clinicians and researchers, would provide the intellectual property to power their software. 

But we thought they were undervaluing what we were bringing to the table. So we actually went to Baylor College of Medicine, and they spun us off as a company, as our own company, from Baylor Technologies.

I learned a lot from trying to start my first company. Not all the learnings were good, but they were important.

What I learned is, one, if you’re going to start a startup, you have to devote all your time to it. You can’t do it on the side. I was heavily involved as a clinician and an administrator, and I was trying to do this on the side.

Second, you must have alignment of your board. We had five board members—me, two from Baylor, and two from the company that was helping us on the IT side. Well, the people on the IT side and the people at Baylor College of Medicine were diametrically opposed on the direction, and we got stuck and couldn’t move forward.

So after about running through a million bucks, the company died. But what’s interesting is that there are a couple of startups just getting going with the same idea we proposed and were trying to move forward 10 years ago.

So, the idea was the right one, but the alignment was poor, and I wasn’t committing full-time to it. Those are really important lessons for anyone trying to start. 

Q: Great lessons learned there. Yeah. And then, David, you were talking about how AI is, you know, going to be changing the landscape, and you have a certain way of looking at it, especially for healthcare systems. So we’d love to learn your thoughts about that—about AI, GenAI, and where you think it is going, and how one should, you know, kind of embrace it and adopt it to get more value. 

David:  So I am extremely excited about the potential for artificial intelligence to help us provide healthcare because there are so many repetitive processes in healthcare that are being done by people that probably can be done by technology.

When I think about a health system—and, you know, I was running a hospital—about the different places where artificial intelligence could have a major impact, I break it down really into four different areas. This is general; there’s overlap.

So the first one is obviously back-office functions—things like revenue cycle, coding, billing, documentation, pre-authorization. All those things that have to do with revenue cycle.

Then I look at the front end or access—giving patients the opportunity to interact with a provider, giving them the opportunity to schedule, how they’re going to pay for their healthcare, et cetera.

And then on the care delivery side, there are things that help make care easier to deliver, and then there are things that actually help provide care.

So let’s break down those two things. When I say make it easier to deliver care, I’m talking about things like artificial intelligence scribes or ambient listening, ambient dictation.

When I talk about things that help actually deliver care, I’m looking at adjuncts to help read X-rays. So one use case that’s fairly well-developed is the issue around mammography. An important fact and something that radiologists look at in mammograms is breast density.

Breast density sometimes predicts further cancer down the road. Radiologists are really bad at quantitating breast density, and there’s a total lack of correlation from one radiologist to another. But using artificial intelligence, you can improve the ability to determine breast density to almost 95 percent accuracy.

So that is a place where artificial intelligence could augment care and improve how care is delivered and the diagnostic accuracy. That’s what I mean when I say help facilitate care versus actually help deliver care.

Q: Understood. And David, in particular, just for a moment to segue into all the new generative AI technologies that are coming our way. It’s not just about text; it’s also about so many different forms of care and media that it can transform. So any thoughts on where you think generative AI might play a larger role or be more conducive in the healthcare setting?

David: Yeah, so in terms of generative AI, one of the solutions we implemented is something called AVO. And what AVO does is ambient listening—it helps physicians to write the note. But that’s just part of it; that’s not really the full generative AI component.

What it then does is pull relevant information from the electronic medical record to create a real functioning note. It puts in front of the clinician a differential diagnosis, as well as the clinical pathways related to that diagnosis. So yes, it’s ambient listening, which is important, but it’s also natural language processing. It helps to generate feedback, a differential diagnosis, and then the proper treatment pathway for that patient.

It also presents the note in a way that facilitates coding, billing, etc., for the hospital or system to get reimbursed. That has been a game changer for our patients.

Thinking about implementation, though, the implementation of any new tool is really, really challenging. What we found with the ambient listening and ambient dictation is that there was a different level of adoption between specialists like surgeons and primary care doctors. Why do you think that is? I’ll throw it back at you.

Q: I couldn’t even guess that. First of all, I’m thinking to myself, is it the primary care physicians who adopt it more, or is it the—yeah?

David: It’s the specialists and the surgeons who adopt it more readily. For them, a note is something they have to do; it’s not really what they want to do. They want to focus on their operation, their procedure, etc. So, for them, the note is more of a barrier to doing what they really want to do.

For a primary care doctor, one of their primary goals is to create a detailed note. So, for them, the nuances around creating the note are really important. Whereas for a surgeon, they don’t care as much if it’s perfect—they just want to get it done. So, the adoption was much quicker with surgeons than with primary care physicians.

We also implemented it in the emergency room, which is a tight space with a lot going on. Some of the ambient listening got distracted because of other noises in the environment. We had to figure out ways to ensure that, during the interaction between the clinician and the patient, there was as little ambient noise as possible. But we worked through that, and the adoption was incredible.

People have talked about AI replacing physicians. Five years ago, someone came out and said that within five years, there wouldn’t be any need for radiologists. Well, clearly, that hasn’t happened. But I see AI being used as an adjunct to clinicians—helping them to be better at what they do, not just in terms of efficiency, but also in improving accuracy in care and diagnoses.

I think clinicians are going to become partners with artificial intelligence. Another area where I think AI, generative AI, augmented reality, and virtual reality are going to have a huge role is in education.

Even in teaching within the operating room. Right now, the way you teach in the operating room is by working with a resident across from you. Over time, you give them more and more experience and freedom to conduct the procedure. But think about this: if you have really good augmented reality, you could have them work with a headset in an environment outside of the operating room to hone their skills in a space where there’s no risk to patients.

So, there are amazing opportunities for augmented reality and virtual reality, not just in education, but also in surgery. For instance, I used to do a lot of liver surgery. The liver is a three-dimensional structure, but the inputs—like where the vessels are or where the tumor is—come from 2D images. These are from CT scans, MRIs, or ultrasounds.

Then, once you’re in the operating room, you’re trying to reconstruct that 2D image in your mind into a 3D picture, relating it to the organ in front of you. It’s challenging, but you get better at it with experience. Now, with augmented reality, you can actually see, right in front of you, where the tumor is in relation to other structures within the organ.

This has been really well-developed in neurosurgery, but it’s an exciting opportunity for AI, virtual reality, and augmented reality in other types of surgery as well.

Q: Yeah, great example, David. So, my last question is about your new venture or the new role you’re going to be taking on soon, as you described earlier in the podcast. Which aspect are you more excited about? Because you’ve seen the startup side of things, and now you’ve been in a large health system. Where do you think you’ll be spending more of your time and adding value?

David: So, I think healthcare is slow to change from the inside. A number of the bigger systems have developed innovation labs and are trying to move the field forward, but I think a lot of the change is going to come from outside—from companies outside of healthcare.

What I’ve decided to do is move away from running a healthcare system or hospital to being more involved in the digital health and AI ecosystems. I think the way I can best contribute is as an advisor to early-stage companies within this space, but also as an advisor to investors and venture companies looking at which technologies are actually going to make a difference.

Since I have the perspective of both a clinician and an operator within the health system, I think I bring a unique lens to identifying which digital health solutions are really going to make an impact.

Q: Awesome opportunity, David. I’m wishing you all the best in this role? And then would you have any other closing thoughts for our audience? 

David: I think we are at an inflection point, between technology and the desperate needs within the health system. The costs have risen dramatically, people do not have adequate access and there are huge problems within our health system. I think we have opportunity right now to make a difference. 

To move healthcare forward and to improve the healthcare system within this country, I encourage people who are thinking about careers within healthcare to think about the innovation side and the digital health side, because that is really going to have a huge effect and move the needle. 

We hope you enjoyed this podcast. Subscribe to our podcast series at www.thebigunlock.com and write to us at info@thebigunlock.com   

Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.